AI-generated code often works on the first run, which is exactly why it needs a security review before release. The code can look correct while hiding problems that only show up in production.
Check the common failure points
- Secrets. API keys and tokens hard-coded into the app or committed to the repository.
- Data storage. Sensitive data written to plain files, logs or unprotected local storage.
- Authentication. Tokens that never expire, or sessions that are not validated on the server.
- Permissions. Requests for location, camera or contacts that the feature does not need.
- Dependencies. Libraries added by the AI without checking their maintenance status or known vulnerabilities.
Test the built app, not just the source
Review the compiled app as well as the source. Some issues only appear in the packaged binary, such as bundled secrets or debug settings left enabled. Security researchers have written about these risks in their analyses of AI-generated mobile apps, including NowSecure's review of whether vibe-coded mobile apps are easy or secure.
Make it part of the release
Add automated scans, a manual review of high-risk areas and a sign-off step before every external beta. A review is much cheaper before launch than after a data incident.